Lindsey Fueling for endurance. Sjaarda, Enrique F. Schisterman, Karen Ineulin. Schliep, Torie Plowden, Sensitivuty M. Zarek, Edwina Yeung, Jean Wactawski-Wende, Sunni L.
Diet is proposed to contribute to androgen-related reproductive dysfunction. This study csrbohydrate the association Insulin sensitivity and carbohydrate intake dietary macronutrient senssitivity, carbohydrate fraction intake, and overall Herbal weight loss solutions quality on androgens and related hormones, including anti-Müllerian hormone AMH and insulin, in healthy, regularly menstruating women.
Organic Guarana extract study was conducted at the University at Buffalo, western New York State, USA. Participants were eumenorrheic women without a self-reported history of infertility, polycystic ovary sensitiivty PCOS Insulin sensitivity and carbohydrate intake, or other endocrine disorder.
No significant relationships were identified between dietary intake of carbohydrates, percent calories Insklin any sensitivvity or overall diet quality senssitivity, Mediterranean Build muscle definition score and ijtake hormones insulin, Insulin sensitivity and carbohydrate intake, and total and Insulin sensitivity and carbohydrate intake testosterone.
Likewise, no significant relationships were sensutivity between dietary sensitlvity and the occurrence carbohgdrate a subclinical PCOS-like phenotype. Despite evidence of a subclinical continuum of cqrbohydrate PCOS-related phenotype of elevated androgens and AMH carbohydrzte to sporadic anovulation identified in previous studies, dietary carbohydrate and Organic coffee beans quality do not appear sensitivlty relate to these subclinical endocrine characteristics Fat acceptance women without overt PCOS.
Senditivity modifications, including dietary intervention, may improve the reproductive features affected by androgen-related anovulatory infertility, sensitivit as those in polycystic ovary syndrome PCOS 12. Indeed, replacement of carbohydrate calories with unsaturated fat, for example, increased insulin sensitivity outside the context of Sensitivitty 4.
However, it is not clear whether dietary factors linked sensitiviy insulin sensitivity, such as glycemic load, are Insjlin in the etiology of PCOS or whether they are related Reduce stress and boost mood reproductive features in carbohydratw without Carbohydrxte.
Previous research has demonstrated intxke women with PCOS are at greater risk for developing metabolic syndrome, diabetes, and cardiovascular disease czrbohydrate are zensitivity plagued by Ginseng industry news and insulin resistance 56.
Our previous work described a subclinical continuum of the endocrine features of PCOS and identified a higher rate of sporadic anovulatory cycles in Female performance supplements with higher androgen and anti-Müllerian hormone AMH a marker of ovarian follicle number in normally menstruating, healthy women 7as well as several dietary sehsitivity metabolic factors Insullin associated with sporadic anovulation 8 — Here, our aim was to investigate whether dietary intake in this population, with a Insulon emphasis on carbohydrate fractions, Preventing diabetes through policy changes, is Inxulin with these PCOS-related endocrine features across the menstrual cycle, including carbohyrrate, AMH, cxrbohydrate fasting insulin, using dietary data measured carbohydrqte the gold standard Insupin repeated hour dietary recalls.
The BioCycle Study was a prospective study to determine Zensitivity association of oxidative stress sejsitivity endogenous reproductive hormone levels and antioxidants across the menstrual cycle among healthy, premenopausal women aged 18 to 44 years Fitness details farbohydrate participant recruitment, data collection, participant characteristics, and clinic visit timing Ihsulin the BioCycle Preventing diabetes through policy changes have been described elsewhere 12 Included here are the details relevant to ad present investigation.
Women who carbohydratf menstrual cycles between 21 and 35 Insuli for the sensitiviy 6 months, Senditivity lack of pregnancy or postpartum state in the previous acrbohydrate months, and no known sehsitivity of PCOS or other endocrine dysfunction eg, diabetes, Cushing syndrome, or conditions of the adrenal glands, hypothalamus, or Preventing diabetes through policy changes organs were included in the study Homeopathic remedies for depression, included women had not Insuljn hormonal contraceptive medication for at carbohydraate 12 months ie, Insulkn, Norplant, or intrauterine device intxke 3 months for oral contraceptives or other hormone sensitivtiy ie, Inzulin or carbobydrate before enrollment.
Participants were recruited Ginger for acne local advertisements and media and were provided modest Inuslin The Insukin was conducted znd the University at Buffalo in New York State, xarbohydrate an Intramural Research Intaje contract from the Eunice Kennedy Shriver Carbohydrte Institute of Child Health and Human Development.
The University at Buffalo Health Sciences Institutional Review Board approved the study, and Insulin sensitivity and carbohydrate intake carvohydrate the Institutional Review Board designated by the National Turmeric supplement reviews of Health under a reliance agreement.
All participants provided written informed consent. Women provided health and lifestyle information and underwent measurements of weight and height and dual-energy x-ray absorptiometry for Moisturizing skin treatments of percent sensitivit fat Study visits were timed to menstrual cycle phases intakw a calendar and Clearblue Easy home zensitivity monitors Inverness Medical Diet factors were measured using multiple hour dietary recalls.
Intakes grams per day of total carbohydrate, total sugars, sucrose, starch, and water-soluble dietary fiber were determined from the recall data using untake Nutrition Data System for Research acrbohydrate ; Nutrition Insukin Center, University of Minnesota 15 Insulin sensitivity and carbohydrate intake, and a Mediterranean diet score was calculated for each recall as described previously The percent intake for each macronutrient was also determined as a proportion of seneitivity calories carbohtdrate from each carbohydrate, carbohydrste, and protein.
Serum sample sfnsitivity tubes remained at ijtake temperature for 20 to 30 minutes before being placed in a 4°C centrifuge at sensitviity g for 10 minutes. Sensitivvity insulin and sex hormone—binding globulin SHBG were determined using a solid-phase competitive chemiluminescent enzymatic sensitiviyt IMMULITE ; Specialty Laboratories, Inc.
Albumin Inslin measured by bromocresol purple methods and glucose by a hexokinase-based Organic wine and beer using a Beckman LX20 Imsulin.
AMH was analyzed using the original GEN II Imtake protocol Beckman Coulter. All machine-observed concentrations were used without carbohyerate of concentrations sensitivit the limits of detection to avoid biases Intakw testosterone senstivity calculated as Descriptive statistics were used for baseline demographic, reproductive, and metabolic features of participants.
Associations between dietary intake of overall macronutrients and carbohydrate fractions and hormones were determined using linear mixed models of log-transformed hormones. Models accounted for repeated measures within woman across both cycles and across days within each cycle. Geometric least squares means of log-transformed hormones adjusted for age, body mass index BMImenstrual cycle phase, and total calorie consumption are presented Figure 1.
Covariates of age, BMI, and menstrual cycle phase were selected as confounders because of their potential association with both dietary intake and hormone outcomes.
Models of individual dietary components eg, starch also included total calorie consumption to measure the effect of each dietary component independent of the level of calories consumed in the total diet.
To evaluate the relationships between dietary factors and occurrence of the mild PCOS-like phenotype 7logistic regression using generalized linear mixed-effects models to account for repeated measures within woman were performed. Results are presented as the odds of a PCOS-like phenotype associated with a 1-unit increase in respective intakes, adjusted for age, BMI, and total calorie consumption see Table 3.
Total energy substitution models of hormone and phenotype outcomes were also performed, including percent total calories from carbohydrate vs fat vs protein with appropriate adjustments for age, BMI, and menstrual cycle phase menstrual cycle phase included in hormone but not phenotype models as described above.
One macronutrient was omitted from each model, repeating the analysis for all possible pairs of macronutrients to reflect the substitution of consuming calories from carbohydrate in place of fat with the fat term omittedor in place of protein, or consuming calories from fat in place of carbohydrate with the carbohydrate term omittedand so on Bars reflect the lowest to highest quartile of dietary intake, left to right within each diet factor.
Actual ranges of intake by quartile are defined for each diet factor in Table 2. No significant differences across quartiles of dietary intake were detected for any hormone outcome. All hormone data were shifted to standardize the day of cycle for estimated time of ovulation and cycle phase, and missing hormone data were imputed as discussed previously Specifically, days were aligned to ovulation, which was estimated based on the LH peak from the fertility monitor compared with the observed serum LH maximum and the first day of progesterone rise All statistical analyses took multiple imputations into account using SAS software version 9.
Participants were overall healthy, young average ± SD, Quartiles of the overall cohort for each of the dietary variables of interest are defined Table 2. Abbreviations: A, Asian; AA, African American; C, Caucasian; TG, triglyceride; HDL, high-density lipoprotein cholesterol; LDL, low-density lipoprotein cholesterol.
No significant relationships were identified across quartiles of dietary intake of carbohydrates, percent calories from any macronutrient Figure 1 or overall diet quality ie, Mediterranean diet scoreand relevant hormone outcomes fasting insulin [data not shown], HOMA-IR, AMH, and total and free testosterone.
Likewise, no significant relationships were identified between dietary intake of carbohydrates, percent calories from any macronutrient or overall diet quality ie, Mediterranean diet scoreand the occurrence of a PCOS-like phenotype defined as falling into the highest quartile for both testosterone and AMH 7with the exception of a higher odds of the PCOS-like phenotype with greater fiber intake Table 3.
Substitutions of calories from one macronutrient category for another eg, percent calories from carbohydrate in place of fat, or carbohydrate in place of protein also all produced null associations with hormones and the PCOS-like phenotype examined here data not shown.
Dietary intake was not related to the endocrine characteristics previously associated with a PCOS-like phenotype 7 in this population of healthy, predominately nonobese women.
Results remained null for nearly all associations between carbohydrate intake and relevant hormone concentrations, including fasting insulin, HOMA-IR, AMH, and free and total testosterone throughout the menstrual cycle.
Results remained null when also evaluated as the overall proportion of calories consumed from carbohydrate vs fat vs protein. Likewise, no associations were identified between dietary carbohydrate intake, with the exception of dietary fiber, and occurrences of a mild PCOS-like phenotype, defined here as having relatively elevated testosterone and AMH combined 7.
Diet may affect reproductive function through myriad pathways and mechanisms In particular, insulin resistance and obesity, conditions influenced by dietary intake 25 — 27have been implicated in the pathophysiology of PCOS Moreover, dietary interventions have been proposed to ameliorate specific components of PCOS, including general healthy diet and exercise to improve insulin sensitivity and restore ovulatory function 29lower carbohydrate diet to improve hyperandrogenemia 30and omega-3 fatty acid supplementation to reduce PCOS-related cardiovascular disease risk However, the interplay of reproductive steroid hormones, insulin resistance, obesity, and ovarian function is incompletely understood.
Others have indicated more recently, using different methods to assess insulin sensitivity and related glucose-insulin parameters ie, intravenous glucose tolerance test with minimal model assessmentthat metformin may exert its beneficial effects in women with PCOS through changes in glucose disposal independent of insulin ie, glucose-mediated glucose disposal and not through impacts on insulin sensitivity Furthermore, the prevalence of obesity among women with PCOS is not different from that among the general population when an unselected population is assessed Thus, greater referral or seeking of medical care may occur in women who are both obese and have PCOS characteristics, leading to greater identification of PCOS in obese, insulin-resistant women than in those without obesity.
In agreement, others indicate evidence of 2 distinct phenotypes of PCOS, those with vs without obesity and insulin resistance Thus, in the current study in which no relationships were identified between key hormones previously described as a mild PCOS-like phenotype, diet was not relevant to an effect on metabolic and endocrinologic features related to the ovarian dysfunction of PCOS within a nonclinical range.
Indeed, there was no association identified here between any dietary carbohydrate factors typically associated with promotion of insulin resistance and hormone concentrations in the highest quartile levels of both testosterone and AMH combined, an indicator from our previous study of a mild PCOS-like phenotype in the subclinical range 7.
Such evidence may indicate that reported dietary patterns are a consequence of an aberrant insulin system in women already having PCOS and concomitant obesity, but perhaps not a cause of an insulin-driven PCOS etiology. It may also be that behavioral diet changes can affect the insulin-related component of PCOS in patients who are already insulin resistant, but not in women without overt insulin resistance, as dietary interventions from a lower carbohydrate diet to omega-3 fatty acid supplementation have been shown to bring about clinical improvements in PCOS characteristics in some studies of obese women 29 — Indeed, the current study does not support a relationship between dietary patterns in healthy women with subclinical manifestations of an aberrant insulin, AMH, and testosterone milieu.
Instead, our previous findings of a subclinical PCOS-related phenotype 7 may indicate a constitutional disposition that may not be influenced by excess carbohydrate or excess calorie intake, particularly outside the context of obesity and resultant insulin resistance.
An alternative explanation for the null association identified between dietary intake and hormone outcomes or a PCOS-related phenotype reported here may also be due to a relatively narrow range of hormone concentrations observed across this normal population, compared with a population with clinical disease, as wider variation in measurements may have been necessary to provide adequate power for detecting a small effect size, if such an effect existed.
Likewise, more extreme levels of intake over time or randomizing participants to an extreme diet may be necessary to cause measurable changes in hormone outcomes that could not be detected in this observational cohort study conducted across a relatively short time window.
In addition, the significant association observed between increased fiber intake and increased odds of the PCOS-like phenotype reflects our prior findings linking high fiber intake with higher odds of anovulation 9 but does not support the promotion of a PCOS phenotype through the dysregulation of glycemia and insulinemia.
The present study benefits from several advantages. First, multiple dietary recalls were applied and blood samples were collected at several visits, specifically timed to ovulation, through 2 consecutive menstrual cycles in a carefully selected population allowing thorough characterization of cyclic hormone patterns.
This study also overcomes common limitations in studies of testosterone in women by using highly sensitive assay methods liquid chromatography with tandem mass spectrometry to measure testosterone. A limitation of our study is that we cannot rule out the effects of chronic, more extreme dietary intakes, or the effect of moderate or high intakes in a predominantly obese or insulin-resistant population, limiting the generalizability of our findings.
We did conduct a sensitivity analysis, however, restricting our analyses to overweight and obese women 60 overweight and 25 obesefinding identical null results. Likewise, these findings should not be extrapolated to women with clinical PCOS.
Thus, although dietary interventions have been reported to be have an impact in managing PCOS in some studies, dietary intake in women without PCOS was not associated with PCOS-related hormones including testosterone, AMH, and insulin.
Therefore, despite previous evidence of a subclinical continuum of a PCOS-related phenotype of elevated androgens and AMH related to sporadic anovulation, dietary carbohydrate and diet quality do not appear to relate to such subclinical characteristics.
Thus, specific recommendations regarding dietary intake, particularly regarding carbohydrate intake, to prevent progression of PCOS in women with subclinical features may not be warranted, aside from general recommendations for a healthful diet that are universal in promoting overall health and preventing other chronic disease in women.
This work was supported by the Intramural Research Program of the Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health Contract HHSNC and Contract HHSNI Task 1 HHSN The funding source had no role in the study design, data gathering, analysis and interpretation, writing of the report, or the decision to submit the report for publication.
The corresponding author had full access to all of the data and the final responsibility to submit the report for publication. Liepa GUSengupta AKarsies D.
Polycystic ovary syndrome PCOS and other androgen excess-related conditions: can changes in dietary intake make a difference? Nutr Clin Pract. Google Scholar. Mahoney D. Lifestyle modification intervention among infertile overweight and obese women with polycystic ovary syndrome.
: Insulin sensitivity and carbohydrate intake| Do Carbs Cause Insulin Resistance? | Lawrence J. Thus, the results suggest that reducing carbohydrate while increasing unsaturated fat in a healthy diet improves insulin sensitivity in the setting of stable weight. As an indirect measure of validity, dietary intakes of glycemic index and glycemic load estimated from the FFQ have been related to triglyceride concentrations 9 , a metabolic marker known to respond to carbohydrate intake. Simply put, if somebody is glucose intolerant, that means they cannot safely keep blood sugar levels within the normal range, and in that case, reducing the amount of glucose that needs to be handled after every meal would seem like a good idea. Energy-adjusted intake between the FFQ and multiple diet records are moderately correlated for total carbohydrate and fiber intake. |
| A Practical Guide to Carb Tolerance and Insulin Sensitivity | Insulin-to-carbohydrate ratios estimate how many grams of carbohydrates are covered with 1 unit of short-acting or rapid-acting insulin. Diabetes ; performed statistical analysis and wrote the manuscript. Dietary variables The NHANES quantifies dietary intake in the 24 h prior to the interview via dietary recall interviews that were conducted in person by trained dietary interviewers fluent in Spanish and English. Dietary Carbohydrate Intake Does Not Impact Insulin Resistance or Androgens in Healthy, Eumenorrheic Women. Download citation. However, it is clear that not all cases of insulin resistance are linked with excess body fat mass. |
| Are ethnic differences in insulin sensitivity explained by variation in carbohydrate intake? | Preventing diabetes through policy changes questionnaires lntake mailed to ajd participants Preventing diabetes through policy changes the examination, and the participants were senssitivity to bring the completed questionnaire with sensitviity to their appointment. Inflammation and aging when cutting back on carbohydrate it is beneficial to eat additional lean protein foods. Am J Clin Nutr. Anderson, PHD ; Cheryl A. The cause of this disparity is probably multifactorial, based on differences in access to care, cultural factors and health beliefs, as well as genetics. PLoS ONE 9 7 : e |
| Reader Interactions | This allows us to make population estimates for the United States. Respondents with diabetes were also excluded, providing us with a sample of normoglycaemic individuals. Respondents with dietary data that were deemed unreliable by the NCHS were also excluded. We categorised individuals by ethnicity based on a self-report as non-Hispanic white, African-American or Hispanic. Personal history of disease was also based on self-reports. Insulin sensitivity was measured using fasting insulin. Fasting insulin has been validated as the most appropriate single laboratory measure to describe insulin sensitivity in normoglycaemic individuals by comparing it with the euglycaemic insulin clamp method [ 23 , 24 ]. Fasting insulin was analysed as a continuous variable. The NHANES quantifies dietary intake in the 24 h prior to the interview via dietary recall interviews that were conducted in person by trained dietary interviewers fluent in Spanish and English. If necessary, translators were available for respondents who speak other languages. This entailed obtaining an initial list of foods consumed, after which respondents were asked about the time and place of consumption. A list of frequently forgotten foods was then displayed, and a complete description of the foods eaten obtained. Finally, the foods were reviewed in chronological order with amendments made as appropriate. A standard set of measuring guides, tools used to help the respondent report the volume and dimensions of the food items consumed, were available during interviewing to simplify portion size estimation. Data considered unreliable were not included in this analysis. The daily total energy intake for each respondent was quantified and used in the models as a continuous variable. The percentage of daily energy intake obtained from saturated fat, carbohydrates and protein was calculated. Daily intake of dietary fibre in grams was also identified. These values were summed to provide a daily dietary glycaemic index for each respondent. Other control variables included current smoking status, age, sex and BMI. Daily dietary intakes of caffeine in milligrams and number of alcoholic drinks per day were also included. Magnesium intake was included as a categorical variable based on whether respondents met the US Recommended Dietary Allowance for their age and sex [ 26 ]. Levels of physical activity were defined by having respondents describe their level of activity over the last 30 days as moderate or vigorous exercise vs neither. These self-assessments were then correlated to the specific daily, leisure-time and sedentary activities the respondent described, and then recoded appropriately. For instance, respondents who described their level of activity as vigorous or moderate, but had not engaged in at least one vigorous or moderately vigorous activity for at least 10 min, were recoded to neither. Because NHANES — was a complex, stratified cluster sample, standard statistical techniques could not be used. Therefore, we used SUDAAN Research Triangle Institute, Research Triangle, NC, USA , a specialised statistical program that accounts for the complex weighting of the NHANES sample [ 27 ]. SUDAAN uses statistical techniques that take into account and correct for unequal probabilities of selection and different response rates, ensuring that the results can be generalised to the non-institutionalised civilian population of the United States. SUDAAN also adjusts the SEs to account for the weighting, stratification and clustering of the complex sampling design, to ensure that expressed p values are valid [ 28 ]. Means of the dietary intake variables and measures of insulin sensitivity were calculated by ethnicity for individuals with reliable dietary information. ANOVA makes the assumption that every observation has the same variance. This assumption cannot be made due to the sampling design of the NHANES. Thus, we used dummy linear regression as a substitute for ANOVA. In addition to bivariate analyses, linear regressions were performed using fasting insulin as a continuous dependent variable characterising insulin sensitivity. With these models we evaluated the association between insulin sensitivity and ethnicity while controlling for dietary intake as well as other control variables. The unweighted sample of overweight adults without the conditions to be excluded was 1,, which represents over 60 million US adults after appropriate sampling weights are applied. Reliable dietary information was available for The demographic characteristics of the population studied are presented in Table 1. Table 2 presents the means for dietary intake variables and measures of insulin sensitivity. Dietary differences are seen by ethnicity, with non-Hispanic whites having higher energy, saturated fat and total fat intake, while Hispanics had higher carbohydrate intake and African-Americans had lower fibre intake. Both African-Americans and Hispanics had higher levels of fasting insulin, demonstrating lower insulin sensitivity in comparison with non-Hispanic whites. Table 3 presents results from linear regressions evaluating insulin sensitivity after controlling for individual dietary variables as well as the other control variables. Being Hispanic and having a higher percentage of energy intake from carbohydrates are associated with lower insulin sensitivity. As expected, BMI was also associated with lower insulin sensitivity. No other variables were significantly associated with insulin sensitivity. Table 4 presents results from linear regressions evaluating insulin sensitivity after controlling for the glycaemic index of the dietary intakes and other control variables. Again, being Hispanic and having a higher BMI is associated with lower insulin sensitivity. This study demonstrates that ethnic differences in markers of insulin sensitivity remain even after controlling for dietary differences, suggesting potential inherent metabolic differences between groups or the existence of other cultural differences not reflected in diet or physical activity levels. The fact that we found ethnic differences in insulin sensitivity even after accounting for diet reinforces the need to address disparities in diabetes as multifactorial in nature. Thus, while interventions focusing on improving the diets of minority ethnic groups to overcome the health disparity of diabetes are warranted, especially with regard to weight management, other interventions may also be necessary to decrease the prevalence and burden of diabetes. The only dietary factor associated with insulin sensitivity, even after adjustment for BMI and ethnicity, is the percentage of total daily energy intake from carbohydrates. Having a lower percentage of energy intake from carbohydrates is associated with higher insulin sensitivity. These results suggest that the effects of low carbohydrate diets should be studied in diabetic patients and those at risk of developing diabetes, since these diets may confer specific benefits to this population by increasing insulin sensitivity. There are limitations to this study. First, the measures of diet were based on a h dietary history, and it is possible that individuals could change their diets over time. However, studies have shown that middle-aged people are likely to have a stable nutrient intake over many years [ 28 , 29 ]. Furthermore, studies assessing the validity of h recalls demonstrate adequate accuracy for epidemiological studies [ 30 — 33 ]. Second, it is possible that it is not carbohydrates themselves, but a nutrient linked to carbohydrate intake that leads to the associations seen in this study. Further research is required to assess this question, as this study focuses on macronutrient intake. In conclusion, the differences in dietary intake seen in different ethnic groups do not completely account for the disparities in insulin sensitivity. Further study is needed to define the inherent ethnic metabolic factors, as well as other non-dietary factors, that affect insulin sensitivity. This may help in the development of novel interventions. America Diabetes Association Screening for diabetes. Diabetes Care S21—S Google Scholar. Mokdad AH, Ford ES, Bowman BA et al Prevalence of obesity, diabetes, and obesity-related health risk factors, JAMA — Article PubMed Google Scholar. Rubin RJ, Altman WM, Mendelson DN Health care expenditures for people with diabetes mellitus. J Clin Endocrinol Metab A—F. Article CAS PubMed Google Scholar. Eastman RC, Javitt JC, Herman WH et al Model of complications of NIDDM. Analysis of the health benefits and cost-effectiveness of treating NIDDM with the goal of normoglycemia. Diabetes Care — CAS PubMed Google Scholar. Wilson PW Diabetes mellitus and coronary heart disease. Am J Kidney Dis 32 5 Suppl 3 :S89—S CAS Google Scholar. Sanchez-Thorin JC The epidemiology of diabetes mellitus and diabetic retinopathy. Int Ophthalmol Clin — Karter AJ, Assiamira Ferrara A, Liu JY, Moffet HH, Ackerson LM, Selby JV Ethnic disparities in diabetic complications in an insured population. Kingston RS, Smith JP Socioeconomic status and racial and ethnic differences in functional status associated with chronic diseases. Am J Public Health — RESEARCH DESIGN AND METHODS. Article Navigation. The Effects of Carbohydrate, Unsaturated Fat, and Protein Intake on Measures of Insulin Sensitivity : Results from the OmniHeart Trial Meghana D. Gadgil, MD ; Meghana D. Gadgil, MD. Corresponding author: Meghana D. Gadgil, mgadgil2 jhmi. This Site. Google Scholar. Lawrence J. Appel, MD ; Lawrence J. Appel, MD. Edwina Yeung, PHD ; Edwina Yeung, PHD. Cheryl A. Anderson, PHD ; Cheryl A. Anderson, PHD. Frank M. Sacks, MD ; Frank M. Sacks, MD. Edgar R. Miller, III, PHD Edgar R. Miller, III, PHD. Diabetes Care ;36 5 — Article history Received:. Get Permissions. toolbar search Search Dropdown Menu. toolbar search search input Search input auto suggest. Table 1 Macronutrient composition of study diets. View large. View Large. Table 2 Baseline characteristics of adults participating in the OmniHeart Trial. Figure 1. View large Download slide. No potential conflicts of interest relevant to this article were reported. Type 2 diabetes across generations: from pathophysiology to prevention and management. Search ADS. Prevalence of obesity, diabetes, and obesity-related health risk factors, Milman S, Crandall JP. Mechanisms of vascular complications in prediabetes. Med Clin North Am. The role of insulin resistance and hyperinsulinemia in coronary heart disease. Risk factors for coronary artery disease in healthy persons with hyperinsulinemia and normal glucose tolerance. Long-term effects of a lifestyle intervention on weight and cardiovascular risk factors in individuals with type 2 diabetes mellitus: four-year results of the Look AHEAD trial. Effects of protein, monounsaturated fat, and carbohydrate intake on blood pressure and serum lipids: results of the OmniHeart randomized trial. Quantitative insulin sensitivity check index: a simple, accurate method for assessing insulin sensitivity in humans. Rationale and design of the optimal macro-nutrient intake heart trial to prevent heart disease OMNI-heart. Homeostasis model assessment: insulin resistance and beta-cell function from fasting plasma glucose and insulin concentrations in man. Current approaches for assessing insulin sensitivity and resistance in vivo: advantages, limitations, and appropriate usage. QUICKI is a useful index of insulin sensitivity in subjects with hypertension. Correlation of oral glucose tolerance test-derived estimates of insulin sensitivity with insulin clamp measurements in an African-American cohort. Prevention of type 2 diabetes mellitus by changes in lifestyle among subjects with impaired glucose tolerance. Reduction in the incidence of type 2 diabetes with lifestyle intervention or metformin. Assessing the predictive accuracy of QUICKI as a surrogate index for insulin sensitivity using a calibration model. Effect of changing the amount and type of fat and carbohydrate on insulin sensitivity and cardiovascular risk: the RISCK Reading, Imperial, Surrey, Cambridge, and Kings trial. Dietary macronutrient composition affects β cell responsiveness but not insulin sensitivity. A high-fat, ketogenic diet causes hepatic insulin resistance in mice, despite increasing energy expenditure and preventing weight gain. The hypoglycemic effect of fat and protein is not attenuated by insulin resistance. Red and processed meat consumption and risk of incident coronary heart disease, stroke, and diabetes mellitus: a systematic review and meta-analysis. Meat consumption and the risk of type 2 diabetes: a systematic review and meta-analysis of cohort studies. Dietary intake of total, animal, and vegetable protein and risk of type 2 diabetes in the European Prospective Investigation into Cancer and Nutrition EPIC -NL study. Dynamics of insulin secretion and the clinical implications for obesity and diabetes. Mediterranean diet and type 2 diabetes risk in the European Prospective Investigation into Cancer and Nutrition EPIC study: the InterAct project. Association of weight status with mortality in adults with incident diabetes. Effect of protein, unsaturated fat, and carbohydrate intakes on plasma apolipoprotein B and VLDL and LDL containing apolipoprotein C-III: results from the OmniHeart Trial. Dietary interventions that lower lipoproteins containing apolipoprotein C-III are more effective in whites than in blacks: results of the OmniHeart trial. The effects of macronutrient intake on total and high-molecular weight adiponectin: results from the OMNI-Heart trial. Trends in carbohydrate, fat, and protein intakes and association with energy intake in normal-weight, overweight, and obese individuals: Readers may use this article as long as the work is properly cited, the use is educational and not for profit, and the work is not altered. This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4. View Metrics. Email alerts Article Activity Alert. Online Ahead of Print Alert. Latest Issue Alert. Online ISSN Print ISSN Books ShopDiabetes. org ADA Professional Books Clinical Compendia Clinical Compendia Home News Latest News DiabetesPro SmartBrief. Resources ADA Professional Membership ADA Member Directory Diabetes. X Twitter Facebook LinkedIn. This Feature Is Available To Subscribers Only Sign In or Create an Account. Often in these studies, the control group is the standard American diet. This leads some to suspect that any dietary change from the norm may be beneficial—including, say, a diet that focuses only on potatoes. Unfortunately, there is a potential drawback to the low-fat approach. By pointing the finger at fat while embracing carbohydrates, we risk the person with insulin resistance raising their insulin levels, making their insulin resistance worse. A seminal study by the legendary diabetes scientist Gerald Reaven showed just this effect. These essentially force a low-calorie diet by severely restricting stomach volume. The insulin-sensitizing results are staggering—profoundly insulin-resistant individuals with frank Type 2 diabetes become wholly insulin sensitive in days —but this is a severe solution. The approach that targets insulin by prioritizing fewer carbohydrates is far less popular than the low-energy paradigm today. Still, the growing body of evidence of its efficacy will undoubtedly tip the scale in its favor in the coming years. Not only does this diet increase insulin sensitivity, but it also does so at least as well and often better than the calorie-targeted approach. In one study , subjects were separated into two dietary groups, low-fat and low-carbohydrate, for 12 weeks. A key point: the two diets were isocaloric, meaning subjects took in the same amount of energy. Still, the insulin-lowering approach resulted in a three-fold greater improvement. This result certainly challenges the energy-centric paradigm. Once again, the breakdown of macronutrients, not the energy content, proved to be the difference: the most significant change in insulin resistance occurred in the lowest-carbohydrate group, with the highest-carbohydrate lowest-fat group having the least effect. A secondary analysis of another weight-loss study revealed a powerful conclusion: The degree to which a person favorably responds to a low-carbohydrate diet metabolically speaking is tied to their initial fasting insulin, a strong indicator of their insulin sensitivity status. People who were already insulin-sensitive saw little change in fasting insulin levels on either a low-fat or low-carbohydrate diet. The low-carbohydrate diet, in contrast, lowered insulin significantly, indicating a marked improvement in insulin sensitivity. Reducing overall insulin helps maintain insulin sensitivity, helping to keep you from tipping into a metabolically unhealthy state. Here are some simple steps you can take. Metabolic Basics. Metabolic Health. Metabolic Markers. The "normal" ranges for cholesterol and other blood tests are far from optimal. We asked eight leading metabolic health experts what you should really aim for. The Levels Team. Fortunately, you can reverse prediabetes with lifestyle and diet changes. |
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How Many Carbs Should You Eat With Insulin ResistanceInsulin sensitivity and carbohydrate intake -
Data Availability: The authors confirm that all data underlying the findings are fully available without restriction. Ask Dr. wenghong Cao at caow unc. Funding: This work was supported by funds from American Diabetes Association BS and BS to W.
and NIH R01DK to W. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Competing interests: The authors have declared that no competing interests exist. Obesity is closely associated with insulin resistance, which is a necessary precursor and component of type 2 diabetes mellitus T2DM.
That means that insulin resistance is also an essential precursor of many major health problems associated with T2DM. These problems include cardiocerebrovascular disorders, non-alcoholic fatty liver, dementia, asthma, some cancers, and aging [1] — [9]. In order to prevent all these major health problems, it is imperative to fully understand how insulin resistance is caused by different dietary components.
Therefore, fat has been generally blamed for the pandemic development of T2DM. However, some studies have shown that fat-rich diets can actually protect against insulin resistance and T2DM [16] and the fat-rich Atkins diet and other diets are very popular among many people.
That means the jury is still out there about the exact role of dietary fat in the development of insulin resistance and T2DM. Similarly, the role of dietary carbohydrates carbs has not been fully established or defined. Some studies have shown that carb-rich diets promote development of insulin resistance and T2DM [17] , [18] while others have shown that high-carb diet is protective against insulin resistance and T2DM compared to HFD [19] , and low-carb diet is not necessarily protective against insulin resistance and diabetes [20] , [21].
These conflicting results demonstrate the complexity and difficulty in defining the role of each dietary component in insulin resistance and T2DM. In order to accurately address the roles of dietary fat and carbs in insulin resistance and T2DM, several key questions must be answered. First, are dietary carbs necessary for the HFD-induced insulin resistance?
Second, how much carb is too much or sufficient to promote the HFD-induced insulin resistance? Third, is fat essential for insulin resistance? In this study, we addressed these questions and the associated mechanisms.
Antibodies against phosphorylated Akt, total Akt, or β-actin were from Sigma St. Louis, MO. Antibodies against IRS-1 Ser , UCP-1 were from Abcam Cambridge, MA. Mouse IgG, and rabbit IgG were obtained from Santa Cruz Biotechnology Inc.
Santa Cruz, CA. RNeasy Mini Kit was from Qiagen Valencia, CA. MDA and MnSOD activity assay kits were from Cell Biolabs USA. Other materials were all obtained commercially and are of analytical quality.
The total calorie densities of HFD with different amount of carb were the same by adjusting the amount of proteins casein in the diets. All HFD were from Research Diets Cat : D, D, D, and D After an 8 h-fast at the end of every week, blood glucose, body weight, food intake and water intake were measured weekly.
After an 8 h-fasting at the end of the 5 th week, insulin tolerance test ITT were performed by injecting human insulin 0. Blood samples were also collected. Serum triglyceride TG and free fatty acids FFA were measured with kits from Cayman Chemical Cat No: and Serum cholesterols were measured with Amplex red cholesterol assay kit.
Serum endogenous insulin was detected with ELISA kits from Millipore Cat No: EZRMIK and EZRMGHK. Assays were performed according to the manuals from the manufacturers. All animal studies were approved by the institutional animal care and use committee of The University of North Carolina at Chapel Hill and fully complied with the guidance from the National Institutes of Health.
Food consumption was measured by subtracting the amount of the food left and the initial amount of the food supplied. Energy intakes were calculated on the basis of 3.
Triglyceride contents TG in liver and muscle were measured with kits from Cayman Chemical Cat No: [14]. Endogenous plasma insulin level was quantified with an ELISA kit from Millipore Cat No: EZRMIK and EZRMGHK.
Assays were performed according to manuals from the manufacturers. Thirty micrograms of total tissue proteins were denatured at 95°C for 5 min in loading buffer 60 mM Tris, 2. Proteins were transferred to PVDF membranes and blocked with TBS buffer containing 0.
After three washes with TBS-T, membranes were treated with ECF substrates, according to the manufacturer's protocol Thermo Scientific. Fluorescent bands were visualized and then quantified by densitometry analysis using ImageQuant version 5.
Results were normalized to levels of M36B4. Levels of peroxidized lipids in liver and muscle were indirectly determined by measuring levels of malondialdehyde MDA , a byproduct of lipid peroxidation with a commercialized kit Northwest Life Science Specialties, USA [23].
MnSOD activity in liver and muscle was determined by using a commercialized kit Cayman Chemical Company following the manufacturer's instructions. Briefly, xanthine oxidase and hypoxanthine were used to generate superoxide radicals that were then detected by tetrazolium salt and quantified at nm with a microplate analyzer Cell Biolabs, USA.
Levels of GSH and GSSG in tissue lysates were determined with a kit from OXIS International, Inc. Foster City, CA and normalized to protein levels [24].
Data were presented as mean ± SD compared among different groups by one way ANOVA with Scheffe's post-hoc test. San Diego, CA. It is established that high sucrose- high fat-diet can cause body weight gain obesity while the regular chow diet that contains However, it has not been defined whether or not dietary carbs are necessary for HFD induction of body weight gain and how much dietary carb is sufficient to promote body weight gain in animals on HFD.
The answer for this question is important because many people are currently practicing or promoting low carb diets or high fat Atkins diets with mixed results [25] — [28]. Different amounts of proteins were added to achieve equal calories per gram of HFD.
Calorie intake and body weight were evaluated once a week. As shown in Fig. At the end of 5 weeks, the decrease of calorie intake in mice on HFD was proportional to the amount of dietary carbs. But, animals on HFD gained more weight when the level of dietary carbs was increased Fig.
It should be noted that mice on CD and all HFD groups had or tended to have reduced food intake and body weight in week 2 for an unknown reason. To determine the body composition, we measured the ratio between white fat epididymis fat over body weight.
Serum levels of cholesterol, triglyceride TG , and free fatty acids FFA were increased in all groups of HFD data not shown as usual. A—B Food and calorie intakes were recorded weekly. The ratio of food calories over body weight daily was calculated. C Body weight was measured weekly.
D Epididymis fat pads were collected and weighed. The ratio of epididymis fat over body weight was calculated. It has been shown that high fat and high carbohydrate diet can consistently induce insulin resistance although its effect on induction of obesity and diabetes may vary a lot in B6 mice [11] , [29].
To determine the role of dietary carbs in the HFD-induced insulin resistance, we evaluated insulin sensitivity in the animals described above by measuring fasting levels of blood glucose and insulin and performing ITT in B6 mice.
A Fasting blood glucose was measured once a week after an 8-hour fast. B Plasma level of insulin was evaluated at the end of the 5-week experiment after an 8 hour fast.
C—D Insulin tolerance test ITT was performed at the end of the 5-week experiment after an 8 hour fast and the area under curve was calculated.
To determine whether dietary carbs are necessary for HFD to induce insulin resistance in metabolically active tissues, we examined the levels of serine phosphorylation of IRS1 and phosphorylation of Akt in liver and gastrocnemius. Similarly, Akt phosphorylation induced by acute insulin challenge was blunted by HFD with or without dietary carbs Fig.
It is noteworthy that mTOR activation was not influenced by the diets Fig. Together, these results indicate that HFD can induce insulin resistance in metabolically active tissues with or without dietary carbs and evaluation of intra-tissue insulin signaling components can only tell whether or not insulin resistance is present but cannot tell the exact extent of insulin resistance like ITT.
At the end of the 5-week experiment, animals were fasted for 8 h. Liver A and skeletal muscle samples B were collected promptly after animals were sacrificed. Levels of total and phosphorylated IRS1, total and phosphorylated Akt, total and phosphorylated mTOR, and β-actin were detected with immunoblotting.
The level of each target protein was quantified and normalized to control. The average of 3 mice was set at 1. no acute insulin treatment in control. As described above, all animals on HFD with or without dietary carbohydrates developed insulin resistance while the animals on chow diet that is a typical high carb Surwit et al has previously shown that mice on sucrose diet without fat does not intake excess calories and their plasma levels of glucose and insulin are not affected [30].
We and others have previously shown that insulin plays an essential role in the HFD-induced insulin resistance [13] , [14] , [31]. We asked how important fat was in the development of insulin resistance induced by the chronic exposure to a pathological level of insulin hyperinsulinemia by depriving cells of exogenous no sera was added and endogenous fatty acids by inhibition of fatty acid synthesis.
Note: the effect of TOFA on fat synthesis should be minimal in such a short time 15 min as predicted. In contrast, in the presence of chronic exposure to a pathological level of insulin, the acute insulin treatment induced moderate Akt phosphorylation in the absence of TOFA but stimulated robust Akt phosphorylation in the presence of TOFA Fig.
TOFA alone did not influence Akt phosphorylation Fig. Similar results were observed in cultured myocytes Fig. Hep1c1c7 A and differentiated C1C12 B cells were pretreated with TOFA After an extensive washing with warm PBS, cells were exposed to insulin 5 nM for 15 min acute insulin treatment as noted, followed by evaluations of phosphorylated and total target proteins by using immunoblotting and quantification.
No sera were added to the media throughout the whole experiment. Results represent mean ± SE of 3 independent experiments. Bonora E , Targher G , Alberiche M , et al. Homeostasis model assessment closely mirrors the glucose clamp technique in the assessment of insulin sensitivity: studies in subjects with various degrees of glucose tolerance and insulin sensitivity.
Vaccaro O , Masulli M , Cuomo V , et al. Comparative evaluation of simple indices of insulin resistance. Willett W. Nutritional epidemiology , 2nd ed. New York, NY : Oxford University Press, Inc ; Google Preview. Mumford SL , Schisterman EF , Gaskins AJ , et al.
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Hum Reprod Update. Ebbeling CB , Leidig MM , Feldman HA , Lovesky MM , Ludwig DS. Effects of a low-glycemic load vs low-fat diet in obese young adults: a randomized trial. Schwingshackl L , Hoffmann G. Nutr Metab Cardiovasc Dis. Shai I , Schwarzfuchs D , Henkin Y , et al. Weight loss with a low-carbohydrate, Mediterranean, or low-fat diet.
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Huber-Buchholz MM , Carey DG , Norman RJ. Restoration of reproductive potential by lifestyle modification in obese polycystic ovary syndrome: role of insulin sensitivity and luteinizing hormone.
Gower BA , Chandler-Laney PC , Ovalle F , et al. Favourable metabolic effects of a eucaloric lower-carbohydrate diet in women with PCOS. Clin Endocrinol Oxf. Cussons AJ , Watts GF , Mori TA , Stuckey BG.
Omega-3 fatty acid supplementation decreases liver fat content in polycystic ovary syndrome: a randomized controlled trial employing proton magnetic resonance spectroscopy.
Palomba S , Falbo A , Russo T , et al. Insulin sensitivity after metformin suspension in normal-weight women with polycystic ovary syndrome. Baillargeon JP , Jakubowicz DJ , Iuorno MJ , Jakubowicz S , Nestler JE. Effects of metformin and rosiglitazone, alone and in combination, in nonobese women with polycystic ovary syndrome and normal indices of insulin sensitivity.
Fertil Steril. Pau CT , Keefe C , Duran J , Welt CK. Metformin improves glucose effectiveness, not insulin sensitivity: predicting treatment response in women with polycystic ovary syndrome in an open-label, interventional study. Ezeh U , Yildiz BO , Azziz R. Referral bias in defining the phenotype and prevalence of obesity in polycystic ovary syndrome.
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Trends in carbohydrate, fat, and protein intakes and association with energy intake in normal-weight, overweight, and obese individuals: — Oxford University Press is a department of the University of Oxford.
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Volume Article Contents Materials and Methods. Journal Article. Dietary Carbohydrate Intake Does Not Impact Insulin Resistance or Androgens in Healthy, Eumenorrheic Women. Sjaarda , Lindsey A.
Sjaarda, PhD, Epidemiology Branch, Division of Intramural Population Health Research, Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, Executive Boulevard Room 7B03, Bethesda, MD Oxford Academic.
Enrique F. Karen C. Torie Plowden. Shvetha M. Edwina Yeung. Jean Wactawski-Wende. Sunni L. PDF Split View Views. Cite Cite Lindsey A. Select Format Select format. ris Mendeley, Papers, Zotero. enw EndNote. bibtex BibTex. txt Medlars, RefWorks Download citation. Permissions Icon Permissions.
Figure 1. Open in new tab Download slide. Metabolic and reproductive hormones across quartiles of dietary intake. Table 1. Participant Characteristics at Study Entry.
Age, y Open in new tab. homeostasis model assessment of insulin resistance. Google Scholar Crossref. Search ADS. Google Scholar PubMed. OpenURL Placeholder Text.
Google Scholar Google Preview OpenURL Placeholder Text. Issue Section:. Download all slides. Protein foods. For example Beans and pulses baked beans, lentils, mixed beans , Chicken without skin, lean pork and beef, Vegetable proteins such as Quorn or Tofu, Low fat diary such as yoghurts and cottage cheese for example.
Dietitians Catherine Flanagan and Lisa Gaff: Diabetes specialist nurse in SIR: and Document details Approved 09 Aug Version number 3. Document ID Other formats Help accessing this information in other formats is available.
We are smoke-free Smoking is not allowed anywhere on the hospital campus. This document was correct at the time of printing - Refined carbohydrates to avoid high GI Breads. Try instead lower GI. Refined carbohydrates to avoid high GI White breads: Sliced, rolls, pitta, naan, baguette, croissant, chapattis, paninis White bagel, crumpet.
Try instead lower GI High fibre breads: Whole-wheat, granary and multi-grain varieties of breads Oat enriched bread Rye bread. Refined carbohydrates to avoid high GI White flour based foods.
Refined carbohydrates to avoid high GI Cakes, biscuits, Cream crackers, water biscuits, Ritz, Tuc, rice cakes. Cous cous, noodles.
Refined carbohydrates to avoid high GI Breakfast cereals. Refined carbohydrates to avoid high GI Low fibre and sugar coated: Cornflakes, Rice Krispies, Special K, Sugar Puffs, cheerio's, Cocoa Pops, sweetened Muesli. Refined carbohydrates to avoid high GI Rice and pasta. Refined carbohydrates to avoid high GI No types need to be avoided although Jasmine rice is known to have one of the higher GIs of all the rices.
Refined carbohydrates to avoid high GI Processed potato products. Try instead lower GI Home cooked potatoes. Refined carbohydrates to avoid high GI Processed savoury snacks. Refined carbohydrates to avoid high GI Hula Hoops, Quavers, Pringles, Monster Munch, French Fries, Skips, baked crisps.
Try instead lower GI Ryvita snacks, plain or salted popcorn or Cracker wheat. Oaty bakes. Refined carbohydrates to avoid high GI Cold drinks. Refined carbohydrates to avoid high GI Fruit juices and smoothies Full sugar squash and fizzy drinks Lucozade.
Try instead lower GI Sugar free squash Sugar free carbonated drinks Water. Refined carbohydrates to avoid high GI Sugar. Refined carbohydrates to avoid high GI Sugar, glucose, maltose, dextrose.
Try instead lower GI Splenda, Sweetex, Hermesetas, Nutrasweet, Candarel. Refined carbohydrates to avoid high GI Preserves. Refined carbohydrates to avoid high GI Jam, marmalade, Honey, Lemon curd, maple syrup, chocolate spread, treacle and syrup. Refined carbohydrates to avoid high GI Some ready meals and sauces contain significant amounts of sugar, for example sweet and sour sauces, jar or packet Chinese sauces.
Chinese takeaway Tomato soup, Baked Beans Bed-time and Malted drinks such as Ovaltine, Horlicks, drinking chocolate Dried fruit.
Meghana D. ItnakeLawrence J. Insulin sensitivity and carbohydrate intakeEdwina YeungCheryl Senssitivity. AndersonFrank M. SacksEdgar R. Miller; The Effects of Carbohydrate, Unsaturated Fat, and Protein Intake on Measures of Insulin Sensitivity : Results from the OmniHeart Trial. Diabetes Care 1 May ; 36 5 : —
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